Assessment of the relational strength between triggers detected in physiological signals and the occurrence of atrial fibrillation episodes.

Despite the growing interest in understanding the role of triggers of paroxysmal atrial fibrillation (AF), solutions beyond questionnaires to identify a broader range of triggers remain lacking. This study aims to investigate the relation between triggers detected in wearable-based physiological signals and the occurrence of AF episodes.Week-long physiological signals were collected during everyday activities from 35 patients with paroxysmal AF, employing an ECG patch attached to the chest and a photoplethysmogram (PPG)-based wrist-worn device.

ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions.

The present article proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics.

Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection.

To develop a method for detection of bradycardia and ventricular tachycardia using the photoplethysmogram (PPG). The detector is based on a dual-branch convolutional neural network (CNN), whose input is the scalograms of the continuous wavelet transform computed in 5-s segments. Training and validation of the CNN is accomplished using simulated PPG signals generated from RR interval series extracted from public ECG databases.

High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF.

Background: Consumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases.

Objective: DoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires.

Considerations on Performance Evaluation of Atrial Fibrillation Detectors.

Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance.

Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals.

Detection of atrial fibrillation using a wrist-worn device.

Objective: This study proposes an algorithm for the detection of atrial fibrillation (AF), designed to operate on extended photoplethysmographic (PPG) signals recorded using a wrist-worn device of own design.

Approach: Robustness against false alarms is achieved by means of signal quality assessment and different techniques for suppression of ectopic beats, bigeminy, and respiratory sinus arrhythmia. The decision logic is based on our previously proposed RR interval-based AF detector, but modified to account for differences between interbeat intervals in the ECG and the PPG.

Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes.

Objective: A model for simulating multi-lead ECG signals during paroxysmal atrial fibrillation (AF) is proposed.

Significance: The model is of particular significance when evaluating detection performance in the presence of brief AF episodes, especially since annotated databases with such episodes are lacking.

Approach: The proposed model accounts for important characteristics such as switching between sinus rhythm and AF, varying P-wave morphology, repetition rate of f-waves, presence of atrial premature beats, and various types of noise.

Modeling of the photoplethysmogram during atrial fibrillation.

A phenomenological model for simulating the photoplethysmogram (PPG) during atrial fibrillation (AF) is proposed. The simulated PPG is solely based on RR interval information, and, therefore, any annotated ECG database can be used to model sinus rhythm, AF, or rhythms with premature beats. A PPG pulse is modeled by a linear combination of a log-normal and two Gaussian waveforms.

Photoplethysmography-Based Method for Automatic Detection of Premature Ventricular Contractions.

This work introduces a method for detection of premature ventricular contractions (PVCs) in photoplethysmogram (PPG). The method relies on 6 features, characterising PPG pulse power, and peak-to-peak intervals. A sliding window approach is applied to extract the features, which are later normalized with respect to an estimated heart rate.